A 2-year Postdoc position in the field of Signal Processing is open within the department of electrical and computer engineering at Aarhus University, Denmark.
The role involves pioneering new concepts, theories, and techniques to generalize standard nonstationary signal processing tools for graph signals. In addition to the fundamental (algorithmic) contributions in the exciting new field of graph (or network) signal processing, you will get the opportunity to apply those techniques in diverse applications, including the analysis of brain activity signals and vibration analysis of data from cluster-based sensor networks.
Please see further details in the link below and feel free to inform your colleagues or friends who may be interested. The deadline for application submission is June 18, 2022.
Our group will partner with IRRAS Inc and AU Hospital to develop a novel technology to quantify brain compliance using the IRRAflow system. The combination of compliance calculations and IRRAflow treatment may provide real-time data that determines effectiveness and completion of therapeutic treatment. More details on this link.
Our recent article, published in IEEE Transactions on Emerging Topics in Computational Intelligence, proposes a nover motor imagery electroencephalogram (EEG) classification method based on multivariate variational mode decomposition. You can find the article at the this link.
Our recent research article published in IEEE Transactions on Signal Processing presents a new signal denoising method designed specifically for multichannel or multivariate data sets. The method is unique in that it fully utilizes inter-channel correlations within multiple variates of the input data set. Given the ubiquity of multichannel data sets in disparate application areas, owing to the rapid recent developments in sensor technologies, specialized multichannel algorithms have gained vital importance. Here is the link to the paper.
Our article titled “FPGA-Based Design for Online Computation of Multivariate Empirical Mode Decomposition” has been published in IEEE Transactions on Circuits and Systems I: Regular Papers. The article proposes a novel FPGA based architecture of a popular nonstationary signal processing algorithm, multivariate empirical mode decomposition (MEMD). The architecture would pave the way towards the utility of the MEMD algorithm in many real-life online applications where the algorithm has already proven its prowess e.g., biomedical engineering, condition monitoring, signal denoising etc. Here is the web link.
Our recent publication in IEEE Transactions on Signal Processing, titled Multivariate Variational Mode Decomposition, introduces a generic extension of the variational mode decomposition (VMD) algorithm to multichannel data sets. The main feature of the algorithm is its ability to not only decompose multichannel data into its inherent principal modulated oscillations, but to also align common frequency modes present across multiple channels. That greatly facilitates subsequent signal processing on the decomposed components in many real-world applications e.g., data fusion, biomedical signal classification etc.